How manufacturers make the most of machine data
For many manufacturers, there is a disconnect between what goes on in their factories, including their engineering departments, and the core business processes supported by their ERP systems. It creates significant lag times for management to access, analyze and act on data from the manufacturing and development processes. Not having this data in real time could create problems with planning, inventory control, the supply chain or meeting customer expectations.
Barriers to incorporating data from machines on the shop floor into ERP are dropping. Much of the newer equipment is now internet-enabled, and some older machines can be adapted for connectivity. Companies like GE and Siemens are working to standardize platforms for machine-to machine communication. The leading ERP vendors have all taken advantage of this new connectivity to incorporate the machine data into relevant workflows.
So why aren’t all manufacturers connecting their shop floors to their ERP systems? The same old reasons for avoiding any significant technology project: cost, resistance to change and lack of understanding of the ROI.
Complexity can be a factor, too. Mike Lackey, global vice president of solutions management for SAP, gives the example of a company that has dozens of machines from multiple vendors. “The true value [of digital transformation] is tying all the machines together to see what they are producing, the cost structure, performance, and the quality of the output,” he says. “You can’t look at the data off the machines in silos.”
Industrial digitization and its impact
“Industrial digitization concerns two dimensions or core processes,” says Magnus Wilkerson, professor of production systems at Matardalen University in Sweden. “First, the order-to-delivery process, or operational process, integrates data across system layers and throughout the value chain. Critical activities are the integration of MOM/MES (manufacturing operations system/manufacturing execution system) layer into the architecture as well as the supply chain data integration. Second, the industrial digitization concerns the product and production development process. It integrates data across development platforms and stakeholders and enable virtual builds of new products and virtual verification of new processes.”
Those stakeholders might be people internal to the organization such as product managers, engineers or planners. They could also be external such as contract manufacturers, suppliers, or partners. Lackey spoke of an SAP customer, a large medical device manufacturer, that was designing and building a large and highly specialized piece of equipment used in cancer treatment. Because the manufacturing and engineering processes were digitized and connected, what SAP calls a component of Industry 4.0, all the stakeholders from the customer to the people designing and building the unit were on the same page.
The stakes were high. “Their client [a hospital] had spent more than $1 million setting a room up for this equipment,” says Lackey. “With S/4 HANA, bottlenecks and shortages were identified sooner allowing the manufacturer to respond faster managing customer expectations and insuring an on-time delivery.”
The tools to measure ROI are available. Robert Sinfield, director of portfolio marketing at ERP vendor Epicor Software, gives the example of rubber and plastics manufacturing. “It’s very repetitive manufacturing, and manufacturers want to maximize efficiency,” he says. “Solutions to measure efficiency give visibility into the manufacturing process. They can detect deviations in small volumes of time that normally you would not be able to identify.”
For example, the system might identify a machine running at 3 percent lower efficiency over the past three days and send an alert to the service department. That drop might not have been noticed if the machine were not connected to the internet with a monitoring solution that was integrated with the manufacturer’s ERP system. This is important, because even a tiny increase in efficiency in a high-volume manufacturing process can yield significant returns.
Scrappage, or material wasted due to quality issues in the manufacturing process, is another area where intelligence at the machine level can improve efficiency. Sinfield says one Epicor customer cut its scrap average from around 4 percent to 1.37 percent, which also contributed to a 3.1 percent decrease in downtime and consequently lower cost of sales. Annual cost savings for this company were $600,000. “This puts the ROI down to months,” says Sinfield.
Connecting people as well as data
Getting a steady flow of actionable data from the shop floor to business managers and back is important, but just having access to that data might not always be enough. That’s why Epicor has embedded a social framework to support teams associated with a project or process. “Social is built into the fabric of Epicor. It supports a fundamental shift to allow interaction both internally and externally,” says Sinfield. “It’s not just about collecting and analyzing information. It’s really about letting a team collaborate around a project.”
Take a custom engineering-to-order project, for example. Working out the specifications would involve engineers, procurement, and manufacturing. Quality assurance personnel might be needed later on. Customer service and sales need insight into the process to make sure product and delivery expectations are met. “These are all normally quite disparate points with disparate systems outside ERP,” says Sinfield.
When something fails during the process, alerts might go to the project engineer, the sales engineer, and maybe an outside consultant. They are all able to dialog within Epicor ERP, pulling in things like business objectives around the project or the original engineering breakdown when needed. If a fault in a material is found, the supplier might also be brought into the loop within the system.
At some point, finance might get an alert that a project is delayed. They can see through the ERP system exactly what is causing the delay and make decisions about planning or change forecasts based on what they see. “That’s where it really starts to make sense to bring [data] together in ways we were never able to do before,” says Sinfield.
Implementation considerations and challenges
Wilkerson outlined four key challenges for companies looking to digitize their manufacturing processes.
1. The integration of digitization into the operational management and improvement systems. “The technology needs to support the production systems of the companies in their continuous improvement and not build additional layers of management systems,” he says.
2. The management and transformation of legacy systems. “In digitizing a ‘brown field’ manufacturing site, you struggle with numerous systems,” says Wilkerson. “Investments are often made in specific situations. It is necessary to use these windows of opportunity in a conscious development towards a smart factory vision.
3. Ensure robustness in the systems and do not build in sensitive technologies that endanger the delivery or quality of manufacturing.
4. Setting up pre-engineering platforms, test rigs, and demonstration capabilities within production development. “This has been a natural element within product development for decades but not seen as necessary in production development,” says Wikerson. “With the emergence of new technologies while maintaining absolute delivery precision in existing processes, pre-engineering platforms for testing and validating new technologies is central to speed up adoption of technology and best practices.”
Sinfield advises companies considering implementing a system to integrate manufacturing intelligence with their ERP systems to consider two metrics: efficiency and quality. “Companies need to take a step back and ask what they want to see,” he says. “They may not know where they need to be focused.”
In manufacturing, new technology can sometimes be a tough sell to the people on the shop floor, but that might not be the case with digital transformation. Lackey told of one high-value SAP deployment. “We thought we would get push-back from the shop floor,” he says. “But the shop floor supported it. It made the job easier, and the project was successful because of that buy-in.” He attributed that support to frustration using existing antiquated systems.
The prospect of having more data available might seem overwhelming to some stakeholders in the manufacturing process. However, the digital transformation of industry isn’t simply about making more data available. It’s about making the right data available when it will do the most good. “We’re living in the age of information overload,” says Sinfield. “Having the concept of in-context information is incredibly important to derive value for the business.”